Related Experiment Video
Updated: Oct 2, 2025
Digital PCR for Quantifying Circulating MicroRNAs in Acute Myocardial Infarction and Cardiovascular Disease
Published on: July 3, 2018
Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated
Jie Liu1,2,3,4, Lei Huang2,3,4,5, Xinrong Shi1
1Clinical Laboratory Department, The Third Central Hospital of Tianjin, Tianjin, China.
Insights
A new model using seven metabolites and six clinical factors accurately predicts survival in ST-segment elevation myocardial infarction (STEMI) patients. This tool aids early risk stratification after percutaneous coronary intervention.
Area of Science:
- Cardiovascular Medicine
- Metabolomics
- Biomarker Discovery
Background:
- Risk stratification for ST-segment elevation myocardial infarction (STEMI) post-percutaneous coronary intervention is complex.
- Early identification of in-hospital outcomes in STEMI patients is crucial for timely intervention.
- Metabolic profiling offers a novel approach to understanding STEMI patient prognoses.
Purpose of the Study:
- To characterize metabolic fingerprints in STEMI patients with varying in-hospital outcomes.
- To develop a prognostic prediction model integrating clinical and metabolic data.
- To enhance early risk stratification for STEMI patients undergoing percutaneous coronary intervention.
Main Methods:
- Retrospective analysis of plasma samples from two propensity score-matched STEMI cohorts.
- Ultra-performance liquid chromatography-mass spectrometry for metabolic profiling.
- Multivariate analysis using Support Vector Machine (SVM) and Generalized Estimating Equation (GEE) models.
Main Results:
- Distinct metabolic clusters identified between STEMI patients with different in-hospital outcomes.
- Seven differentially expressed metabolites showed high predictive capability (AUC = 0.998 with SVM, 0.981 with GEE).
- An integrated model combining metabolites and clinical data further improved prediction accuracy (AUC = 0.99).
Conclusions:
- A powerful early survival prediction model for STEMI patients can be developed using seven metabolites and six clinical indicators.
- This integrated model shows significant potential for improving risk stratification in STEMI patients.
- Further validation in internal and external cohorts is necessary to confirm the model's generalizability.
Background:
Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.
Methods:
Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.
Results:
Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983-1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969-0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981-0.998).
Conclusion:
A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.
More Related Videos
11:02Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
18:11A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
Published on: December 28, 2012
Related Concept Videos
01:20Blood Studies for Cardiovascular System I: Cardiac Biomarkers
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:19Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
01:19Acute Coronary Syndrome II: Pathophysiology and Clinical Manifestations
01:30Acute Coronary Syndrome I: Introduction
01:27Myocarditis II: Clinical Features and Diagnostic Tests